Improved generalisation using cooperative learning and rule extraction

Rule extraction from artificial neural networks (ANN) addresses the need for symbolic representations to explain an ANN´s decisions. The ANNSER rule extraction approach extracts rules from feedforward ANN that are used for classification. The quality of the ANNSER rules reflects the strengths and weaknesses of the ANN and indicates those classes over which the ANN did not generalize well. This paper introduces a new approach to ANN training in which two or more ANNSER learners co-exist in a cooperative multiagent learning environment. The ANNSER learners cooperate by using one another´s high quality rules to generate new training instances. In this way, the generalization of the ANN are improved, leading to a set of high quality rules that describe the knowledge embedded in the trained ANN